Access the full text.
Sign up today, get DeepDyve free for 14 days.
B. Spencer, S. Dyke, M. Sain, J. Carlson (1997)
PHENOMENOLOGICAL MODEL FOR MAGNETORHEOLOGICAL DAMPERSJournal of Engineering Mechanics-asce, 123
Z. Ying, W. Zhu, T. Soong (2003)
A STOCHASTIC OPTIMAL SEMI-ACTIVE CONTROL STRATEGY FOR ER/MR DAMPERSJournal of Sound and Vibration, 259
W. Zhu, Z. Ying, Y. Ni, J. Ko (2000)
OPTIMAL NONLINEAR STOCHASTIC CONTROL OF HYSTERETIC SYSTEMSJournal of Engineering Mechanics-asce, 126
H. Kushner (1978)
Optimality Conditions for the Average Cost per Unit Time Problem with a Diffusion ModelSiam Journal on Control and Optimization, 16
M. Symans, M. Constantinou (1999)
Semi-active control systems for seismic protection of structures: a state-of-the-art reviewEngineering Structures, 21
L. Jansen, S. Dyke (2000)
Semiactive Control Strategies for MR Dampers: Comparative StudyJournal of Engineering Mechanics-asce, 126
W. Zhu, Z. Ying, T. Soong (2001)
An Optimal Nonlinear Feedback Control Strategy for Randomly Excited Structural SystemsNonlinear Dynamics, 24
W. Zhu, Y. Lin (1991)
Stochastic Averaging of Energy EnvelopeJournal of Engineering Mechanics-asce, 117
(2001)
His research interest is nonlinear random vibration and control
B. Erkus, M. Abé, Y. Fujino (2002)
Investigation of semi-active control for seismic protection of elevated highway bridgesEngineering Structures, 24
Yu-Kweng Lin, G. Cai (1967)
Probabilistic Structural Dynamics: Advanced Theory and Applications
Z. Huang, W. Zhu, Yoshiyuki Suzuki (2000)
Stochastic averaging of strongly non-linear oscillators under combined harmonic and white-noise excitationsJournal of Sound and Vibration, 238
Z. Huang, W. Zhu, Y. Ni, J. Ko (2002)
STOCHASTIC AVERAGING OF STRONGLY NON-LINEAR OSCILLATORS UNDER BOUNDED NOISE EXCITATIONJournal of Sound and Vibration, 254
S Dyke, B. Spencer, M. Sain, J. Carlson (1996)
Modeling and Control of Magnetorheological Dampers for Seismic Response ReductionSmart Materials and Structures, 5
G. Yang, B. Spencer, J. Carlson, M. Sain (2002)
Large-scale MR fluid dampers: modeling and dynamic performance considerationsEngineering Structures, 24
W. Zhu, Z. Huang, Yoshiyuki Suzuki (2001)
Response and stability of strongly non-linear oscillators under wide-band random excitationInternational Journal of Non-linear Mechanics, 36
Y. Ni, Yong Chen, J. Ko, D. Cao (2002)
Neuro-control of cable vibration using semi-active magneto-rheological dampersEngineering Structures, 24
S Dyke, B. Spencer, M. Sain, J. Carlson (1998)
An experimental study of MR dampers for seismic protectionSmart Materials and Structures, 7
W. Zhu, M. Luo, L. Dong (2004)
Semi-active control of wind excited building structures using MR/ER dampersProbabilistic Engineering Mechanics, 19
B. Spencer, S. Dyke, M. Sain, J. Carlson (1996)
Phenomenological Model of a Magnetorheological Damper
A stochastic optimal semi-active control strategy for strongly nonlinear oscillator subjected to external and/or parametric excitations of Gaussian white noises using magneto-rheological (MR) damper is proposed. The dynamic behavior of an MR damper is characterized by using the Bouc-Wen hysteretic model. The control force produced by the MR damper is split into a passive part and a semi-active part. The passive part is incorporated with the uncontrolled system to form a passive control system. Then the system is converted into an equivalent nonlinear non-hysteretic stochastic control system, from which an Itô stochastic differential equation for total energy is derived by using the stochastic averaging method of energy envelope. For the ergodic control problem, a dynamical programming equation for the controlled total energy process is established based on the stochastic dynamical programming principle. The optimal control law is obtained by minimizing the dynamical programming equation and can be implemented by the MR damper without clipping. Then the fully averaged Itô equation for the controlled total energy process is obtained by replacing the control force with the optimal control force and completing the averaging. Finally, the response of semi-actively controlled system is obtained from solving the final dynamical programming equation and the Fokker-Planck-Kolmogorov (FPK) equation associated with the fully averaged Itô equation. The efficacy of the proposed stochastic optimal semi-active control strategy is illustrated by using the numerical results for two examples.
Advances in Structural Engineering – SAGE
Published: Dec 1, 2004
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.